Optimizing Node Energy Conservation in WSN Using Chaotic Maps and ABC Algorithm

Problem Definition

From the literature review, it is evident that the current methodologies for enhancing the lifespan of Wireless Sensor (WS) networks have some notable limitations. One major issue is that the selection of Cluster Heads (CHs) in the network is based on only a few parameters, neglecting the multiple factors that play a crucial role in this selection process. Additionally, many existing techniques use optimization algorithms for CH selection, which often suffer from slow convergence rates or getting stuck in local minima. This results in increased complexity and computational time, ultimately leading to a decrease in network performance. Furthermore, the lack of an effective technique for managing the energy consumption of CH nodes is identified as a key challenge, as these nodes play a vital role in collecting, processing, and transmitting data to the sink node.

Without addressing this issue, the network's lifespan is significantly reduced. Therefore, it is imperative to develop a new approach that tackles these issues to improve the overall efficiency and longevity of WS networks.

Objective

The objective of this project is to develop a new approach that addresses the limitations of current methodologies for enhancing the lifespan of Wireless Sensor Networks (WSN). Specifically, the project aims to improve the selection process of Cluster Heads (CHs) by utilizing a combination of the Artificial Bee Colony (ABC) optimization algorithm and Chaotic map technique. By considering parameters such as residual energy, node density, distance to sink node, and throughput, the proposed Chaos-based ABC model aims to select the most suitable CH, leading to reduced energy consumption and increased network lifespan. Additionally, the project introduces a relay node to reduce communication distance and improve data transmission efficiency within the network. This innovative approach is designed to optimize WSN performance and address the challenges identified in existing methodologies.

Proposed Work

The proposed work aims to address the limitations identified in existing methodologies for enhancing the lifespan of Wireless Sensor Networks (WSN). By analyzing the literature, it is clear that the selection of Cluster Heads (CHs) plays a crucial role in determining the network lifespan. Therefore, the focus of this project is to improve the selection process of CHs by implementing a method that brings together the strengths of Artificial Bee Colony (ABC) optimization algorithm and Chaotic map technique. The fusion of these two techniques is aimed at overcoming the slow convergence rate of ABC and improving overall performance. By considering parameters such as residual energy, node density, distance to sink node, and throughput, the proposed Chaos-based ABC model selects the most suitable CH, leading to reduced energy consumption and increased network lifespan.

Furthermore, the proposed approach introduces a relay node to reduce the communication distance between CHs and the sink node. This rechargeable relay node acts as a bridge, enhancing the effectiveness of the network and ensuring efficient data transmission. By combining the improved CH selection process with the addition of relay nodes, the project aims to optimize the performance and extend the lifespan of WSNs. This innovative approach is expected to address the research gap identified in the literature and provide a practical solution to the challenges faced by existing WSN methodologies.

Application Area for Industry

This project can be utilized in various industrial sectors such as agriculture, transportation, healthcare, and environmental monitoring, where Wireless Sensor Networks (WSN) play a crucial role in data collection and monitoring. The proposed solution addresses the challenge of selecting appropriate Cluster Heads (CH) in the network, which directly impacts the lifespan and efficiency of the network. By incorporating the Chaotic map technique with the Artificial Bee Colony (ABC) optimization algorithm, the proposed model overcomes the limitations of slow convergence rate and local minima trapping, ultimately leading to improved performance. The benefits of implementing this solution in different industrial domains include increased network lifespan, reduced energy consumption by CH nodes, and enhanced overall network efficiency. By considering parameters like residual energy, node density, distance to sink node, and throughput, the chaos-ABC model can select the most suitable CH in the network.

Additionally, the integration of relay nodes further enhances the efficacy of the model by acting as a rechargeable bridge between CH and sink node, ensuring continuous and reliable data transmission. Industries can leverage this project to optimize their WSN operations, improve data collection, and enhance monitoring capabilities in a cost-effective and efficient manner.

Application Area for Academics

The proposed project can significantly enrich academic research, education, and training in the field of Wireless Sensor Networks (WSN). By addressing the limitations of existing techniques in selecting Cluster Heads (CHs) and optimizing network lifespan, the project offers a novel approach that can be used as a valuable tool for researchers, MTech students, and PhD scholars. Researchers in the field of WSN can benefit from the proposed chaotic-ABC model by exploring innovative research methods for enhancing network performance and lifespan. The fusion of chaotic map technique with Artificial Bee Colony (ABC) optimization algorithm introduces a new dimension to data analysis and optimization in WSN. The model's focus on selecting the most appropriate CH based on key parameters such as residual energy, node density, distance between nodes, and throughput offers a comprehensive approach to network optimization.

MTech students can utilize the code and literature of this project to gain insights into advanced optimization techniques and simulations within educational settings. By implementing the proposed chaotic-ABC model, students can explore the practical applications of optimization algorithms in selecting CHs and improving network efficiency. PHD scholars can leverage the research contributions of this project to advance their studies in WSN and explore the potential applications of chaotic map techniques in data analysis and optimization. The incorporation of relay nodes in the communication phase adds a new dimension to network design and opens up avenues for further research in rechargeable relay nodes. The combination of chaotic map and ABC algorithm not only enhances the performance and efficiency of the network but also provides a platform for exploring new research methods and simulations in the field of WSN.

The future scope of this project includes further refinement of the model, exploring different optimization techniques, and conducting experiments to validate the effectiveness of the proposed approach in real-world WSN scenarios.

Algorithms Used

The proposed method in this project uses a combination of the Chaotic Map technique and the Artificial Bee Colony (ABC) optimization algorithm to enhance the selection of Cluster Heads (CH) in Wireless Sensor Networks (WSN). The Chaotic Map technique helps improve the convergence rate of the ABC algorithm, which in turn aids in selecting the most suitable CH in the network. By analyzing parameters such as residual energy, node density, distance to the sink node, and node throughput, the proposed model calculates the fitness value to select the best CH. Additionally, the introduction of a rechargeable relay node in the communication phase further enhances the efficiency of the proposed chaotic-ABC model by acting as a bridge between the CH and the sink node. This approach aims to reduce energy consumption and increase the overall lifespan of the network.

Keywords

SEO-optimized keywords: Wireless Sensor Networks, WSN, Clustering protocol, CM-ABC, Cuckoo Search, Artificial Bee Colony, Network lifetime, Energy efficiency, Data aggregation, Data routing, Cluster formation, Cluster head selection, Network topology, Node selection, Energy conservation, Self-organization, Wireless communication, Sensor nodes, Application-specific networks, Energy-aware protocols, Artificial intelligence.

SEO Tags

Wireless Sensor Networks, WSN, Clustering protocol, CM-ABC, Cuckoo Search, Artificial Bee Colony, Network lifetime, Energy efficiency, Data aggregation, Data routing, Cluster formation, Cluster head selection, Network topology, Node selection, Energy conservation, Self-organization, Wireless communication, Sensor nodes, Application-specific networks, Energy-aware protocols, Artificial intelligence, Chaotic map technique, Relay node, Optimization algorithms, Residual energy, Node density, Distance between sensor node to sink node, Throughput of nodes